chore: import upstream snapshot with attribution

2026-07-13 13:36:25 +08:00
commit 26446540fa
3151 changed files with 974126 additions and 0 deletions
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+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name, too-many-locals, too-many-arguments
+"""Deformable convolution in python"""
+
+import itertools
+import math
+
+import numpy as np
+
+from tvm.topi.nn.utils import get_pad_tuple
+
+
+def deformable_conv2d_nchw_python(
+    a_np, offset_np, w_np, stride, padding, dilation, deformable_groups, groups
+):
+    """Deformable convolution operator in NCHW layout.
+
+    Parameters
+    ----------
+    a_np : numpy.ndarray
+        4-D with shape [batch, in_channel, in_height, in_width]
+
+    offset_np : numpy.ndarray
+        4-D with shape [batch, deformable_groups * filter_height * filter_width * 2,
+                        out_height, out_width]
+
+    w_np : numpy.ndarray
+        4-D with shape [num_filter, in_channel, filter_height, filter_width]
+
+    stride : int or a list/tuple of two ints
+        Stride size, or [stride_height, stride_width]
+
+    padding : int or str or a list/tuple of 2 or 4 ints
+        Padding size, or ['VALID', 'SAME'], or
+        [pad_height, pad_width] for 2 ints, or
+        [pad_top, pad_left, pad_bottom, pad_right] for 2 ints
+
+    dilation : int or a list/tuple of two ints
+        Dilation size, or [dilate_height, dilate_width]
+
+    deformable_groups : int
+        Number of deformable groups
+
+    groups : int
+        Number of groups
+
+    Returns
+    -------
+    b_np : np.ndarray
+        4-D with shape [batch, out_channel, out_height, out_width]
+    """
+    batch, in_channel, in_height, in_width = a_np.shape
+    out_channel, _, kernel_h, kernel_w = w_np.shape
+    out_height, out_width = offset_np.shape[-2:]
+    dtype = a_np.dtype
+    ic_per_dgroup = in_channel // deformable_groups
+    assert groups == 1, "deformable_conv2d_nchw_python does not support groups > 1"
+
+    if isinstance(stride, int):
+        stride_h = stride_w = stride
+    else:
+        stride_h, stride_w = stride
+
+    pad_top, pad_left, _, _ = get_pad_tuple(padding, (kernel_h, kernel_w))
+
+    if isinstance(dilation, int):
+        dilation_h = dilation_w = dilation
+    else:
+        dilation_h, dilation_w = dilation
+
+    def _bilinear(n, c, h, w):
+        y_low = math.floor(h)
+        x_low = math.floor(w)
+        y_high = y_low + 1
+        x_high = x_low + 1
+
+        wy_h = h - y_low
+        wx_h = w - x_low
+        wy_l = 1 - wy_h
+        wx_l = 1 - wx_h
+
+        val = 0
+        for wx, xp in zip((wx_l, wx_h), (x_low, x_high)):
+            for wy, yp in zip((wy_l, wy_h), (y_low, y_high)):
+                if 0 <= yp < in_height and 0 <= xp < in_width:
+                    val += wx * wy * a_np[n, c, yp, xp]
+        return val
+
+    a_deform = np.zeros((batch, in_channel, out_height, out_width, kernel_h, kernel_w), dtype=dtype)
+    for n, h, w in itertools.product(range(batch), range(out_height), range(out_width)):
+        offset = offset_np[n, :, h, w].reshape(deformable_groups, kernel_h, kernel_w, 2)
+        in_h = h * stride_h - pad_top
+        in_w = w * stride_w - pad_left
+
+        index_h_base, index_w_base = np.meshgrid(
+            np.arange(in_h, in_h + kernel_h * dilation_h, dilation_h, dtype=offset_np.dtype),
+            np.arange(in_w, in_w + kernel_w * dilation_w, dilation_w, dtype=offset_np.dtype),
+            indexing="ij",
+        )
+
+        for c, kh, kw in itertools.product(range(in_channel), range(kernel_h), range(kernel_w)):
+            dg = c // ic_per_dgroup
+            index_h = index_h_base + offset[dg, ..., 0]
+            index_w = index_w_base + offset[dg, ..., 1]
+
+            y, x = index_h[kh, kw], index_w[kh, kw]
+            if y < 0 or y >= in_height or x < 0 or x >= in_width:
+                continue
+            a_deform[n, c, h, w, kh, kw] = _bilinear(n, c, y, x)
+
+    b_np = np.zeros((batch, out_channel, out_height, out_width), dtype=dtype)
+    for n, c, f, h, w in itertools.product(
+        range(batch), range(in_channel), range(out_channel), range(out_height), range(out_width)
+    ):
+        b_np[n, f, h, w] += np.tensordot(a_deform[n, c, h, w], w_np[f, c])
+
+    return b_np
+
+
+def deformable_conv2d_nhwc_python(
+    a_np, offset_np, w_np, stride, padding, dilation, deformable_groups, groups
+):
+    """Deformable convolution operator in NHWC layout.
+
+    Parameters
+    ----------
+    a_np : numpy.ndarray
+        4-D with shape [batch, in_height, in_width, in_channel]
+
+    offset_np : numpy.ndarray
+        4-D with shape [batch, out_height, out_width,
+                        deformable_groups * filter_height * filter_width * 2]
+
+    w_np : numpy.ndarray
+        4-D with shape [filter_height, filter_width, in_channel, num_filter]
+
+    stride : int or a list/tuple of two ints
+        Stride size, or [stride_height, stride_width]
+
+    padding : int or str or a list/tuple of 2 or 4 ints
+        Padding size, or ['VALID', 'SAME'], or
+        [pad_height, pad_width] for 2 ints, or
+        [pad_top, pad_left, pad_bottom, pad_right] for 2 ints
+
+    dilation : int or a list/tuple of two ints
+        Dilation size, or [dilate_height, dilate_width]
+
+    deformable_groups : int
+        Number of deformable groups
+
+    groups : int
+        Number of groups
+
+    Returns
+    -------
+    b_np : np.ndarray
+        4-D with shape [batch, out_channel, out_height, out_width]
+    """
+    a_np = np.transpose(a_np, [0, 3, 1, 2])  # NHWC -> NCHW
+    offset_np = np.transpose(offset_np, [0, 3, 1, 2])  # NHWC -> NCHW
+    w_np = np.transpose(w_np, [3, 2, 0, 1])  # HWIO -> OIHW
+    b_np = deformable_conv2d_nchw_python(
+        a_np, offset_np, w_np, stride, padding, dilation, deformable_groups, groups
+    )
+    b_np = np.transpose(b_np, [0, 2, 3, 1])  # NCHW -> NHWC
+    return b_np